New challenges in biorobotics: Incorporating soft tissue into control systems

Materials for Smart Soft Actuator Systems

In contrast to conventional hard actuators, soft actuators offer many vivid advantages, such as improved flexibility, adaptability, and reconfigurability, which are intrinsic to living systems. These properties make them particularly promising for different applications, including soft electronics, surgery, drug delivery, artificial organs, or prosthesis. The additional degree of freedom for soft actuatoric devices can be provided through the use of intelligent materials, which are able to change their structure, macroscopic properties, and shape under the influence of external signals. The use of such intelligent materials allows a substantial reduction of a device's size, which enables a number of applications that cannot be realized by externally powered systems. This review aims to provide an overview of the properties of intelligent synthetic and living/natural materials used for the fabrication of soft robotic devices. We discuss basic physical/chemical properties of the main kinds of materials (elastomers, gels, shape memory polymers and gels, liquid crystalline elastomers, semicrystalline ferroelectric polymers, gels and hydrogels, other swelling polymers, materials with volume change during melting/crystallization, materials with tunable mechanical properties, and living and naturally derived materials), how they are related to actuation and soft robotic application, and effects of micro/macro structures on shape transformation, fabrication methods, and we highlight selected applications.

Biomedical soft robots: current status and perspective

This paper reviews the current status of soft robots in biomedical field. Soft robots are made of materials that have comparable modulus of elasticity to that of biological systems. Several advantages of soft robots over rigid robots are safe human interaction, ease of adaptation with wearable electronics and simpler gripping. We review design factors of soft robots including modeling, controls, actuation, fabrication and application, as well as their limitations and future work. For modeling, we survey kinematic, multibody and numerical finite element methods. Finite element methods are better suited for the analysis of soft robots, since they can accurately model nonlinearities in geometry and materials. However, their real-time integration with controls is challenging. We categorize the controls of soft robots as model-based and model-free. Model-free controllers do not rely on an explicit analytical or numerical model of the soft robot to perform actuation. Actuation is the ability to exert a force using actuators such as shape memory alloys, fluid gels, elastomers and piezoelectrics. Nonlinear geometry and materials of soft robots restrict using conventional rigid body controls. The fabrication techniques used for soft robots differ significantly from that of rigid robots. We survey a wide range of techniques used for fabrication of soft robots from simple molding to more advanced additive manufacturing methods such as 3D printing. We discuss the applications and limitations of biomedical soft robots covering aspects such as functionality, ease of use and cost. The paper concludes with the future discoveries in the emerging field of soft robots.

Growing and evolving soft robots

Completely soft and flexible robots offer to revolutionize fields ranging from search and rescue to endoscopic surgery. One of the outstanding challenges in this burgeoning field is the chicken-and-egg problem of body-brain design: Development of locomotion requires the preexistence of a locomotion-capable body, and development of a location-capable body requires the preexistence of a locomotive gait. This problem is compounded by the high degree of coupling between the material properties of a soft body (such as stiffness or damping coefficients) and the effectiveness of a gait. This article synthesizes four years of research into soft robotics, in particular describing three approaches to the co-discovery of soft robot morphology and control. In the first, muscle placement and firing patterns are coevolved for a fixed body shape with fixed material properties. In the second, the material properties of a simulated soft body coevolve alongside locomotive gaits, with body shape and muscle placement fixed. In the third, a developmental encoding is used to scalably grow elaborate soft body shapes from a small seed structure. Considerations of the simulation time and the challenges of physically implementing soft robots in the real world are discussed.